Deep learning-enhanced single-shot triorganelle STED-FLIM imaging of lipid dynamics in living cells.

Deep learning-enhanced STED-FLIM imaging achieves simultaneous, nanoscale visualization of ER, lipid droplets, and mitochondria in living cells with high spatiotemporal resolution.

• Why it matters: Understanding lipid dynamics across organelles is crucial for insights into cell stress responses and disease mechanisms, but current imaging methods lack the resolution and multiplexing needed for live-cell studies.
• What they did: The authors developed a single-shot STED-FLIM workflow combining Nile Red analogs with deep learning-based demultiplexing, enabling compartment-specific mapping and automated segmentation of three organelles in a single acquisition.
• The result: This approach captures coordinated lipid remodeling during stress responses, revealing nanoscale organization and microenvironmental shifts, and paves the way for practical, high-resolution live-cell lipid imaging.